The present disclosure relates generally to the processing of acoustic well log data and the analysis of subsurface formations. More particularly, the present disclosure relates to a product, system, and method for reducing noise in waveforms for improved processing of near-wellbore formation signals.
Noise in the acoustic signal for near-wellbore measurements, such as acoustic logging, can come from a variety of sources. For example, the noise in these near-wellbore measurements can occur as a result of the wellbore inclination, as a result of a particularly rough or “bad” hole, or as a result of drilling or tool noise. There are many geological and operational factors that can cause noise in these measurements. Although some noise may be tolerated in the waveform data and still provide a reasonably accurate processed result, too much noise or certain types of noise in these signals can create large errors in subsequent models or processed results that effect revenue computations, reservoir models, and geomechanical properties. Furthermore, the error bars seemingly acceptable within acoustic log processing, out of context from downstream uses or models, may not be acceptable in downstream models as that error is integrated over larger distances or depths because once that dataset is integrated into larger models, then that discrete error propagates and compounds, often creating large discrepancies in geomechanical properties, reservoir size, or other important data-driven models. Therefore, it is important to view the error in the scope of the larger, downstream implications.
A large source of error for acoustic log processing is the inclusion of noise signal when processing. For example, when computing a common acoustic logging processed product such as slowness, otherwise known as tying time-to-depth, the noise signal skew the processed time-to-depth tie to be faster or slower than is reasonable for the formation. This result would then not correlate accurately with the geological properties of the subsurface formation and would create propagating errors in the seismic models relying on this time-to-depth tie. Seismic data may be used to approximate a time-to-depth tie and is sometimes used to roughly verify the acoustic waveform processing results, but since the error bars are larger due to the measurement being taken on a larger scale, acoustic logging waveform data is more often relied upon.
Noise signals obscure the formation signal and create discrepancies such as the time-to-depth discrepancy. There are filtering methods available and widely used in the industry, but these methods have limitations and are often insufficient in effectively reducing noise in modern operations. They are insufficient in reducing noise due to the overlap in time and frequency of the noise waveforms with the formation waveforms. One example of operations causing particular problematic noise is the noise created from measurement-while-drilling tools. Another example of operations causing particular problematic noise is that caused by particularly rugged or “bad” holes. Another example of operations causing particular problematic noise is the noise caused by data collection in inclined holes. Furthermore, modern operations call for greater accuracy to stay cost-effective and profitable. In these cases, among others, the tools are run at great expense and then the data is often deemed “unusable,” wasting time and money of those in the industry. In the alternative, the data is processed, the companies attempt to use, see discrepancies with other data points gathered and then toss it aside—another great expense in time, money, and resources.
Although the noise often overlaps in time and frequency with the formation, the noise signal often arrives before (preceding) or dominates the waveform data after (anteceding) the formation signal. This difference in time is often due to the fact that the noise signal does not travel through the formation medium and, therefore, does not have the same travel time. These windows in which the noise signal is dominant or the only present signal, provides an opportunity to isolate the noise signal and study its characteristics in isolation from the formation signal.
The present disclosure provides a computer product that can more effectively filter or reduce noise in acoustic logging datasets so that the data collected can be used more often, saving time and money for the companies relying on this data for their other models and computations.
The present disclosure provides a method for reducing noise in acoustic logging datasets so that more of the data collected can be used, saving time and money for the companies relying on this data for their other models and computations, thereby reducing waste.
The present disclosure provides a system for reducing noise in acoustic logging datasets so that more of the data collected can be used, saving time and money for the companies relying on this data for their other models and computations, thereby reducing waste.
The present disclosure may be automated and integrated into the drilling process or it may be utilized after the data has been collected. There is a need for an effective method of noise reduction in the industry, so that companies can make better use of more of their data, thereby reducing waste in time, resources, and energy.